Loog, Marco

26 publications

TMLR 2025 Abstraction for Bayesian Reinforcement Learning in Factored POMDPs Rolf A. N. Starre, Sammie Katt, Mustafa Mert Çelikok, Marco Loog, Frans A Oliehoek

ECML-PKDD 2025 The Vanishing Empirical Variance in Randomly Initialized Deep ReLU Networks Michal Grzejdziak-Zdziarski, David M. J. Tax, Marco Loog

MLJ 2023 Also for K-Means: More Data Does Not Imply Better Performance Marco Loog, Jesse H. Krijthe, Manuele Bicego

TMLR 2023 An Analysis of Model-Based Reinforcement Learning from Abstracted Observations Rolf A. N. Starre, Marco Loog, Elena Congeduti, Frans A Oliehoek

NeurIPS 2023 Why Did This Model Forecast This Future? Information-Theoretic Saliency for Counterfactual Explanations of Probabilistic Regression Models Chirag Raman, Alec Nonnemaker, Amelia Villegas-Morcillo, Hayley Hung, Marco Loog

CVPR 2022 Enhancing Classifier Conservativeness and Robustness by Polynomiality Ziqi Wang, Marco Loog

ECML-PKDD 2022 LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks Felix Mohr, Tom J. Viering, Marco Loog, Jan N. van Rijn

ECCVW 2022 Social Processes: Self-Supervised Meta-Learning over Conversational Groups for Forecasting Nonverbal Social Cues Chirag Raman, Hayley Hung, Marco Loog

AAAI 2021 Consistency and Finite Sample Behavior of Binary Class Probability Estimation Alexander Mey, Marco Loog

UAI 2020 Semi-Supervised Learning, Causality, and the Conditional Cluster Assumption Julius Kügelgen, Alexander Mey, Marco Loog, Bernhard Schölkopf

NeurIPS 2019 Minimizers of the Empirical Risk and Risk Monotonicity Marco Loog, Tom Viering, Alexander Mey

MLJ 2019 Nuclear Discrepancy for Single-Shot Batch Active Learning Tom J. Viering, Jesse H. Krijthe, Marco Loog

COLT 2019 Open Problem: Monotonicity of Learning Tom Viering, Alexander Mey, Marco Loog

AISTATS 2019 Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features Julius Kügelgen, Alexander Mey, Marco Loog

NeurIPS 2018 The Pessimistic Limits and Possibilities of Margin-Based Losses in Semi-Supervised Learning Jesse Krijthe, Marco Loog

MLJ 2017 Projected Estimators for Robust Semi-Supervised Classification Jesse H. Krijthe, Marco Loog

JMLR 2016 Feature-Level Domain Adaptation Wouter M. Kouw, Laurens J.P. van der Maaten, Jesse H. Krijthe, Marco Loog

ECML-PKDD 2010 Constrained Parameter Estimation for Semi-Supervised Learning: The Case of the Nearest Mean Classifier Marco Loog

CVPRW 2009 Bicycle Chain Shape Models Stefan Sommer, Aditya Tatu, Chen Chen, D. R. Jurgensen, Marleen de Bruijne, Marco Loog, Mads Nielsen, François Lauze

CVPRW 2009 Dense Iterative Contextual Pixel Classification Using Kriging Melanie Ganz, Marco Loog, Sami S. Brandt, Mads Nielsen

JMLR 2008 On the Equivalence of Linear Dimensionality-Reducing Transformations Marco Loog

JMLR 2007 A Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion Marco Loog

ECCV 2006 Bony Structure Suppression in Chest Radiographs Marco Loog, Bram van Ginneken

AAAI 2005 Enhanced Direct Linear Discriminant Analysis for Feature Extraction on High Dimensional Data A. Kai Qin, S. Y. M. Shi, Ponnuthurai N. Suganthan, Marco Loog

ECCV 2004 Dimensionality Reduction by Canonical Contextual Correlation Projections Marco Loog, Bram van Ginneken, Robert P. W. Duin

ECCV 2004 Support Blob Machines. the Sparsification of Linear Scale Space Marco Loog